Answers are grounded in this paper and cite their sources.
This paper evaluates the use of a small number of generalist parameter sets as an alternative to classical calibration. Here parameter sets are considered generalist when they yield acceptable performance on a large number of catchments. We tested the genericity of an initial collection of 10 6 parameter sets sampled in the parameter space for the four‐parameter GR4J rainfall‐runoff model. A short list of 27 generalist parameter sets was obtained as a good compromise between model efficiency and length of the short list. A different data set was used for an independent evaluation of a calibration procedure, in which the search for an optimum parameter set is only allowed within this short list. In validation mode, the performance obtained is inferior to that of a classical calibration, but when the amount of data available for calibration is reduced, the generalist parameter sets become progressively more competitive, with better results for calibration series shorter than 1 year.
No takes yet. Share an insight, caveat, or question.
Andréassian et al. (2014) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: